{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.linear_model import LinearRegression"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 生成数据\n",
    "x = np.array(list(range(1, 11))).reshape(-1,1)\n",
    "y = ([5.56, 5.70, 5.91, 6.40, 6.80, 7.05, 8.90, 8.70, 9.00, 9.05])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[5.56, 5.7, 5.91, 6.4, 6.8, 7.05, 8.9, 8.7, 9.0, 9.05]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 模型训练\n",
    "m1 = DecisionTreeRegressor(max_depth=1)\n",
    "m2 = DecisionTreeRegressor(max_depth=3)\n",
    "m3 = LinearRegression()\n",
    "\n",
    "m1.fit(x, y)\n",
    "m2.fit(x, y)\n",
    "m3.fit(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型预测\n",
    "x_test = np.arange(0, 10, 0.01).reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_1 = m1.predict(x_test)\n",
    "y_2 = m2.predict(x_test)\n",
    "y_3 = m3.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 结果可视化\n",
    "plt.figure(figsize=(10, 6), dpi=100)\n",
    "\n",
    "plt.scatter(x, y, label=\"data\")\n",
    "plt.plot(x_test, y_1, label=\"max_depth=1\")\n",
    "plt.plot(x_test, y_2, label=\"max_depth=3\")\n",
    "plt.plot(x_test, y_3, label=\"linear regression\")\n",
    "\n",
    "plt.xlabel(\"数据\")\n",
    "plt.ylabel(\"预测值\")\n",
    "plt.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  },
  "latex_envs": {
   "LaTeX_envs_menu_present": true,
   "autoclose": false,
   "autocomplete": true,
   "bibliofile": "biblio.bib",
   "cite_by": "apalike",
   "current_citInitial": 1,
   "eqLabelWithNumbers": true,
   "eqNumInitial": 1,
   "hotkeys": {
    "equation": "Ctrl-E",
    "itemize": "Ctrl-I"
   },
   "labels_anchors": false,
   "latex_user_defs": false,
   "report_style_numbering": false,
   "user_envs_cfg": false
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
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   "toc_position": {},
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